强大的车载信号质量评估使用多模式信号融合
Laushya Senthilkumar1, Joana M Warnecke2, Julian Bollmann2
1Indian Institute of Technology Madras, India.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究引入了一种使用传感器融合和BiLSTM模型的新方法,用于从驾驶信号中准确检测心跳. 该方法通过克服运动工件和信号噪声来增强驾驶期间的心血管监测.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 通过心电图 (ECG) 持续监测心血管健康提供了频繁检查的替代方案.
- 安装在方向盘上的心电图传感器在驾驶环境中面临着诸如运动器件,噪音和信号丢失等挑战.
研究的目的:
- 开发一种可靠和准确的方法来检测心跳,使用传感器融合和双向长短期记忆 (BiLSTM) 模型.
- 在驾驶场景中使用各种传感器信号和融合技术评估心跳检测的性能.
主要方法:
- 使用了一组数据集,包括参考心电图,方向盘心电图,光电位图 (PPG) 和成像 PPG (iPPG) 信号.
- 使用传感器融合技术与BiLSTM模型相结合,用于R波检测.
- 实验评估了单个信号和融合方法,以确定心跳位置的准确性.
主要成果:
- 在模拟的城市驾驶场景中,BiLSTM模型实现了62.69%的性能.
- 与单个信号相比,传感器融合显示了提高心跳检测精度的潜力.
结论:
- 拟议的传感器融合和BiLSTM模型为驾驶环境中可靠的心跳检测提供了一个有希望的方法.
- 开发的模型可以集成到汽车系统中,用于持续监测心血管健康.
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